Principles of Software-Defined Elastic Systems for Big Data Analytics

Truong, Hong Linh; Dustdar, Schahram

doi:10.1109/ic2e.2014.67

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…Issues such as data visualization, i.e., failure of big data when unusual circumstances such as Tsunami takes place (Villanueva et al, 2014), delay in data fetching from remote storage devices or due to geographical constraints (Li et al, 2015b), lack of big data analytics platform between applications and services to provide data intelligence (Xiong et al, 2014), and lack of appropriate tools and techniques for decision making (Truong and Dustdar, 2014) have been highlighted in literature. In order to deal with such issues, researchers have suggested techniques such as data pre-fetching using Bayesian algorithm which can timely predict the data required by the user and transfer it from remote location to the local cache (Li et al, 2015b), data streaming in real time using glyphs to ensure scalability and modularity of data to overcome the visualization issues (Li et al, 2015b), involvement of humans to visualize the patterns as they can successfully collect the data during unusual circumstances (Li et al, 2015b), and the development of context aware platforms between data sources and services for effective decision making process (Xiong et al, 2014).…”

Section: Varietymentioning

confidence: 99%

“…, data predictability to provide quality services to users (Dobre and Xhafa, 2014;Koh et al, 2015), use of elasticity principle in big data analytics techniques to deal with diversity and distribution of data (Truong and Dustdar, 2014), and development of sophisticated operation center to integrate all type of real data which could provide customized services to users (Li et al, 2015a).…”

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confidence: 99%

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Addressing big data challenges in smart cities: a systematic literature review

Chauhan

Agarwal

Kar

2016

INFO

114

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Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

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Section: Varietymentioning

confidence: 99%

mentioning

confidence: 99%

Addressing big data challenges in smart cities: a systematic literature review

Chauhan

Agarwal

Kar

2016

INFO

114

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“…This also 5 Fig. 6: Nomads Request Sequence Diagram allows to incorporate multiple objectives for optimizations enabling for example, not just finding an instantiation but the optimal one, or one with minimal migrations etc.…”

Section: Framework Architecturementioning

confidence: 99%

“…This is accomplished using Distributed Analytical Environments (DAE). DAEs can be considered an instance of Software-defined Elastic Systems for Big Data Analytics [5]. Such a DAE relies on dynamic analytical service compositions which are created based on specific questions from stakeholders to provide the desired results.…”

Section: Introductionmentioning

confidence: 99%

Nomads - Enabling Distributed Analytical Service Environments for the Smart City Domain

Schleicher

Vögler

Inzinger

et al. 2015

2015 IEEE International Conference on Web Services

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The advent of the Smart City domain has led to the creation of massive amounts of diverse data. Stakeholders in this domain need to be able to analyze this data in order to make informed planning decisions. To address this complex task, Distributed Analytical Environments (DAEs) have emerged. These environments consist of different distributed analytical and data services, which are composed in a dynamic way to deliver insights that are crucial for stakeholders. Since these environments deal with business critical and sensitive information, strict compliance constraints apply. These constraints lead to situations where certain concrete services are not allowed to exchange data, even though their interaction is necessary to produce the desired results. Finding a valid solution in the space of possible instantiations is a non-trivial problem. In this paper we introduce Nomads, a framework that enables service mobility in such constrained dynamic composition environments to overcome aforementioned restrictions. The framework improves the overall satisfiability and therefore also the quality of constrained DAEs. We outline the requirements of a representative DAE scenario, provide a detailed problem formulation, and then discuss the service mobility framework along with our solution finding algorithm. The evaluation demonstrates that the Nomads framework considerably increases the number of successfully performed compositions even in highly constrained environments.

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“…Software defined infrastructure (Kandiraju, et al, 2014;Li, et al, 2014;Alba, et al, 2014;Truong & Dustdar, 2014) is a new concept for architecting IT infrastructure. It composes IT infrastructure from commodity hardware, managed by a software-stack above it.…”

Section: Software-defined Storagementioning

confidence: 99%

Intelligent Software-Defined Storage With Deep Traffic Modeling for Cloud Storage Service

Huang¹,

Huang²,

Chen³

2016

STSC

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The advent of cloud computing, big data, and mobile computing has created a fast-growing demand for storage. Cloud service providers are looking for cost-effective storage solutions as an alternative to traditional, high-cost, embeddedsystems-based storage to meet the needs of newly emerging applications, such as messaging, video streaming, data analytics, etc. In particular, they are facing the challenge of lowering costs while still accommodating multi-workloads on a single instance of storage without compromising workload performance requirements. Software-defined storage (SDS) is a new generation of storage system. Unlike traditional embedded-systems-based storage, the SDS uses a software-stack above commodity hardware to provide more valuable and cost-effective features. To meet the challenges cloud service providers are facing, this paper introduces the architecture of a new SDS platform called Federator. It also argues that the architecture of an SDS platform should have three main characteristics: 1. separation of the control and data pathways, 2. self-configuration of storage resources, and 3. RESTful APIs for new business extension. This paper specifically introduces the storage I/O traffic modeling supported by Federator. With this capability, storage performance metrics are generated by using Long-Short Term Memory (LSTM). This prediction capability is important to a self-configurable SDS to meet performance requirements.

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Principles of Software-Defined Elastic Systems for Big Data Analytics

Cited by 12 publications

References 17 publications

Addressing big data challenges in smart cities: a systematic literature review

Addressing big data challenges in smart cities: a systematic literature review

Nomads - Enabling Distributed Analytical Service Environments for the Smart City Domain

Intelligent Software-Defined Storage With Deep Traffic Modeling for Cloud Storage Service

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